Many current gas analysis techniques are carried out using gas chromatography (GC) coupled with a detection method such as flame ionization, thermal conductivity, thermionic emission, discharge ionization, electron capture, helium ionization, photo-ionization, mass spectroscopy (MS), infrared spectrophotometry, electrolytic conductivity, and other detection techniques. Although all are clearly adequate for certain tasks, several exhibit low sensitivity or are completely insensitive towards certain functional groups or towards non-combustible gases such as H2O, CO2, SO2, and NOx. A number of these methods require expensive, bulky equipment and skilled operators. Others employ a radioactive source which means, in the United States, their use in the workplace, shipping and disposal are all federally regulated. In addition, several are destructive (i.e., the sample being tested is consumed by the test), time consuming with regard to sample collection, transportation, storage and separation, and therefore are not ideally suited for outside laboratory applications, such as on the scene analysis, in-home daily patient monitoring, or prescreening diagnosis in a hospital or clinic. As a result, nearly all of these gas detection methods are inadequate as stand-alone detectors for many specific applications and must be used in tandem in order to obtain the desired information.
An air pollutant is any substance in the air that can cause harm to humans or the environment (Air Pollutants. 9 Sep. 2009. USEPA. 10 Sep. 2009 http://www.epa.gov/ebtpages/airairpollutants.html). Commonly, air pollutants are classified as either primary or secondary. Primary pollutants are substances directly emitted from a process, such as ash from a volcanic eruption, carbon monoxide from a motor vehicle exhaust or sulfur dioxide released from a factory. Secondary pollutants are not emitted directly; instead, they form in the air when primary pollutants react or interact with other compounds. An example of a secondary pollutant is ground level ozone—one of the many secondary pollutants that make up photochemical smog.
In addition, air pollutants may be natural or man-made and may take the form of solid particles, liquid droplets or gases. Examples of common air pollutants include sulfur oxides (SOx), nitrogen oxides (NOx), carbon monoxide, carbon dioxide, volatile organic compounds (VOCs), toxic metals, chlorofluorocarbons (CFCs), ammonia, persistent organic pollutants (POPs), and radioactive pollutants.
The World Health Organization states that 2.4 million people die each year from causes directly attributable to air pollution, with 1.5 million of these deaths attributable to indoor air pollution. It is estimated that worldwide more deaths per year are linked to air pollution than to automobile accidents. Direct causes of air pollution deaths include aggravated asthma, bronchitis, emphysema, lung and heart diseases, and respiratory allergies.
Greenhouse gases are gases in the atmosphere that absorb and emit radiation within the thermal infrared range and therefore greatly affect the temperature of the Earth. The main greenhouse gases in the Earth's atmosphere are water vapor, carbon dioxide, methane, nitrous oxide (N2O), and ozone. The total set of greenhouse gas (GHG) emissions caused by an organization, event or product is known as a carbon footprint. For simplicity of reporting, a carbon footprint is often expressed in terms of the amount of carbon dioxide, or its equivalent of other GHGs, emitted.
Human activities since the start of the industrial era in the mid-18th century have increased the levels of greenhouse gases in the Earth's atmosphere. The 2007 assessment report compiled by the Intergovernmental Panel on Climate Change (IPCC) noted that “changes in atmospheric concentrations of greenhouse gases and aerosols, land cover and solar radiation alter the energy balance of the climate system”, and concluded that “increases in anthropogenic greenhouse gas concentrations is very likely to have caused most of the increases in global average temperatures since the mid-20th century.”
Unfortunately, at the present there are no known efficient and accurate devices or methods for the continuous, real-time detection and quantification of the wide range of components, desirable or undesirable, found in air. For example, the detection of VOCs alone traditionally involves collecting a sample at the site, transporting the sample to a laboratory, and preparing the sample for analysis. While this method is effective and accurate, it cannot be used for continuous on-line analysis to provide information on a real-time basis as required for effective pollution control and for meeting regulatory requirements.
To address these shortcomings, U.S. Pat. No. 5,435,169 (the “'169 patent”), presented a device and method for the continuous, near real-time, monitoring of low level concentration of VOCs in a fluid stream. The method is comprised of the steps of collecting at least one sample of the VOCs from the fluid stream followed by concentrating the collected VOC samples. At predetermined time periods, the concentrated, collected samples of the VOCs are desorbed from the concentration means, and the desorbed, concentrated, collected samples of the VOCs are injected into a detector. The steps are repeated for continuous monitoring.
The device in the '169 patent comprises a multiport sampling valve, a concentrator element and a concentration detector. The valve comprises a sample retention element, with the valve periodically switching to cause a sample of the stream, with contained materials, to enter and be retained in the sample retention element. The valve is further connected to a source of an inert carrier gas, wherein the sample is entrained on the carrier gas and is carried to the concentrator element from the sample retention element.
Although the '169 patent addressed many of the inadequacies, (i.e., collecting, transporting and preparing a gas sample for analysis) of the prior art at the time, the technology is not without its own shortcomings. For example, the invention in the '169 patent requires multiple preparative steps (i.e., concentration, desorption, injection) be performed on a gas sample before it can be analyzed. As a result, analysis is not performed in real time but is governed by the number of steps required to prepare the sample for analysis and the time required for each step to be performed. In addition, in order to detect low concentrations of a particular component in a gas sample the invention requires a concentration step, which in turn requires there be a concentration aspect to the invention. Lastly, the carrier gas requirement in '169 results in increased costs, availability and sample reactivity issues.
The ever increasing demand for greater analytical productivity, i.e., in the clinical, agricultural and pharmaceutical industries, has given rise to the development of a variety of automated analytical instruments. The developments in this field have been stimulated by the many advantages gained through automation, e.g., increased precision, decreased cost per sample, and the increased reliability of automated equipment. Generally, automated sample analyzers can be divided into two main groups: batch analyzers and continuous flow analyzers.
In the batch analyzer, each sample is placed in its individual container where it remains during the course of the analytical procedure. The containers proceed through the instrument, where prior to reaching the detector unit, reagents may be added at predetermined points and times. The sample reaches the detector unit, where the actual measuring procedure is performed. Each sample is evaluated separately in the analyzer, i.e., it operates discontinuously. Some of the disadvantages of batch analyzers are that they contain complex moving parts, and the containers must be washed and/or discarded after use. Additionally, these instruments are far less versatile than continuous flow analyzers.
In continuous flow analyzers, samples are successively aspirated from their individual containers into a tube through which the samples move until the entire analysis is completed. In this way, the samples which successively follow each other become part of a continuously moving stream into which, at predetermined points and times, reagents are continuously added at fixed flow rates. The processed stream finally reaches a flow through cell of a spectrophotometer (or other measuring device) where the signal is quantitated. The greatest advantage of continuous flow analyzers is their simplicity and their versatility which allows an easy programming of the stream (which, for instance, might be split for multiple analyses). The disadvantage of the continuous flow concept is primarily the potential possibility of carry-over.
There are two types of continuous flow analyzers, the air segmented flow analyzer and the flow injection analyzer. The former separates successive samples in a continuous tube by means of air bubbles. Flow injection analysis, is based on the formation and exploitation of concentration profiles of samples injected into an unsegmented carrier stream. This procedure allows for considerably greater sampling rates than those typically found in air segmented flow analysis.
The introduction of an air bubble into a flowing stream made the continuous flow analysis practical. The role of the air bubble is simply to segment the flowing stream and thus minimize carry-over effects. Thus, the continuously flowing stream is regularly and frequently segmented by air bubbles which effectively sweep the tubes, thereby allowing the sampling rate to increase dramatically. A further increase of the sampling rate is hindered by the necessity of reaching, for each individual sample, a “steady state” signal level. Consequently, long sampling times are required in order to achieve the necessary precision of analysis, thus limiting the output of the continuous analyzer; increased, rapid sampling rates resulted in carry-over effects and less precision.
Studies of the kinetic parameters which characterize continuous flow systems have established that the attainment of a “steady state” signal level is not required provided that the sample is introduced into the continuously flowing stream over an exact period of time. However, the utilization of such “transient” signals requires a high precision of sampling, which is difficult to achieve. This is due to the difficulties with precise sampling; irregularities in the pumping action of the pump; and the presence of air bubbles.
The “flow injection analysis” system, a second type of continuous flow analyzer, introduces samples directly into a continuously flowing carrier stream. Unlike much of the prior art where the sampling tube continuously introduces material (sample-air-wash-air-sample, etc.) which then joins a flowing stream of reagents, the flow injection analysis is based upon discrete injection of a well defined volume of sample into a continuously flowing stream of reagents, which is then carried towards a detector. The reagents, necessary for a particular analysis, can be added to the system neat, can be present in a carrier stream into which samples are injected, or can be added at positions further down the line on the way to the detector.
Electromagnetic radiation has been used in a wide array of noninvasive diagnostic applications. X-rays have been used for many years to create a two dimensional image of the inside of an object. Computed axial tomography scanners are able to generate three dimensional images from a series of two dimensional x-ray images. Magnetic resonance imaging (also known as nuclear magnetic resonance spectroscopy), such as disclosed in Harms et al., U.S. Pat. No. 5,415,163 A and Rapoport et al., U.S. Pat. No. 4,875,486 A, operate by first applying a magnetic field to a subject so as to align, in a uniform manner, the nuclei of atoms within a portion of the subject to be tested. These aligned nuclei are then briefly exposed to a radio frequency (RF) signal set to a specific frequency, which causes each of the various aligned nuclei at a lower energy state to spin or flip to a higher energy state, known as a resonant frequency. The magnetic field is then removed or altered, causing the nuclei forced to a resonant frequency to become unstable and return to their original lower energy state. This later process is called spin relaxation. The faint energy released from the spin relaxation is then collected as a representation of the nuclei within the sample.
Hence, the spin relaxation energy released by the sample is used to generate an image that is representative of the sample. The RF signal itself is not utilized for detection or imaging purposes—it is only used to excite the nuclei to a higher energy state and is removed before the spin relaxation energy is detected. Further, the magnetic field(s) are only used to align and then release the nuclei in the sample, and are removed or altered before spin relaxation can occur.
While electromagnetic signals transmitted through a specimen have been used to detect or measure the concentration of various chemicals in that specimen, such prior techniques were not highly accurate and results were often difficult to repeat. For example, U.S. Pat. No. 4,679,426 disclosed a non-invasive technique for measuring the concentration of chemicals, such as sodium chloride, in a sample. Periodic electromagnetic waves between 10 MHz and 100 MHz were coupled to a subject's finger and resulting waveforms were found to be indicative, at specific frequencies (i.e., 17.75 MHz for sodium chloride and potassium chloride), of concentration levels of those chemicals in the finger. Likewise, U.S. Pat. No. 4,765,179 used periodic electromagnetic waves between 1 MHz and 1 GHz, that were coupled to a subject's finger, to generate a waveform that provided meaningful analysis of glucose levels in the subject based on the presence of other compounds in the subject's blood at specific frequencies (i.e., 17.75 MHz for sodium chloride and potassium chloride, 11.5 MHz for ethyl alcohol, etc.).
In U.S. Pat. No. 5,508,203 (the “'203 patent”), high frequency electromagnetic radiation was coupled to a specimen through a probe pair to generate a signal of varying amplitude or phase that could be compared to a source signal to determine the presence of a target chemical, such as NaCl, to help determine glucose levels. While this later technique represented an improvement over the prior methods, it was soon realized that electrolytes, e.g., NaCl, KCl, Na2HPO4, and KH2PO4 of varying concentrations in human blood, can affect the accuracy of glucose measurements using the '203 patent.
To account for the deficiencies in the '203 patent, a new technique was developed in U.S. Pat. No. 5,792,668 (the “'668 patent”), in which two signals were transmitted through the subject at the same time and the magnitude of impedance at the subject was measured to determine a glucose level in the subject. In particular, the two signals had a cross-over frequency of about 2.5 GHz that provided the best measurement of impedance. In blood specimens, it was found that electrolyte concentration effects are effectively “tuned out” by examining impedance at this cross-over frequency. A similar approach was applied in U.S. Pat. No. 7,184,810 (the “'810 patent”), which cites the '668 patent. In the '810 patent, a probe is applied to the subject's skin, through which electric pulses from a pulse generator are fed and partially reflected back to a measuring device, where a time resolved measurement is made. The glucose level is determined from matching the measured voltage to a calibration table.
The next evolutionary step in the development of electromagnetic energy signals to determine the presence and concentration level of chemicals within a subject is represented in U.S. Pat. No. 6,723,048 B2 (the “'048 patent”), which is assigned to the assignees of the present application and which discloses a noninvasive apparatus for analyzing blood glucose and similar characteristics. The '048 patent apparatus utilizes spaced apart transmission and detection nodes placed on either side of and in contact with a sample to be tested. The nodes are always in close proximity to one or more pairs of magnets that create a magnetic field that envelope the nodes and the sample between the nodes. An RF signal having a frequency between 2 GHz and 3 GHz is transmitted from the transmission node through the sample and to the detection node.
The detected signal is then sent to an analyzer that employs pattern recognition techniques to compare the detected signal at a specific frequency (with respect to glucose, the '048 patent specified 2.48 GHz), to previously detected signals at the same frequency to make a determination regarding the characteristic of the sample being tested. For example, if the sample was a finger of a patient that had previously been tested when the patient was known to have different glucose levels (verified through a more traditional form of glucose testing) to create three or more previously detected signal patterns, the presently detected signal would be compared to each of these previously detected signal patterns to determine which pattern it most closely resembled in order to approximate the patient's present blood glucose level.
In addition to testing glucose levels and other blood chemistries, it has been speculated that electromagnetic frequency spectrum technologies could have application to the biometric identification field, but development is still needed in this area. In many fields of activity, it is essential that persons be identified or their claimed identity be authenticated. Examples of such fields include granting physical access or entry into buildings, rooms or other spaces, airport security, credit card purchasers, ATM users, passport verification, electronic access to information or communication systems, etc.
A number of noninvasive detection technologies have been developed to address these needs, such as fingerprint scans, iris and retina scans, and voice recognition. These technologies operate on the principal that individuals possess unique and unchanging physical characteristics that can be measured and compared with stored data. The basic requirements for acceptable biometric technology are that it must allow for practical widespread use, be accurate and reliable, be difficult or impossible to circumvent, be quick, easy and convenient, present no or little privacy violation concerns, be low cost to produce, and be consumer friendly. Current biometric identification and authentication technologies do not meet all of these basic requirements.
Iris and retina scanning technologies can be highly accurate, but the equipment used in scanning is expensive and requires substantial space. Further, humans are highly uncomfortable with the idea of having their eyes scanned with a laser or infrared light or having their picture taken and stored by a machine (and then used by who knows who). Also, iris and retina scanners have been spoofed with a number of techniques that have required the technologies to be modified in various ways, making the technology more expensive, less convenient, and less consumer friendly.
Electronic or optical fingerprint scanning systems are inexpensive, but are not very accurate, are easily damaged, and can be easily spoofed. Variations in skin, ethnic races with very light fingerprint patterns, people with unusually dry skin, elderly people, people with rough hands, water webbing, abrasions and cuts have all been known to create difficulties for fingerprint systems. Furthermore, many people consider fingerprinting to be an invasion of their privacy because of the heavy use of fingerprinting for law enforcement purposes. Additionally, many fingerprint scanning devices have been easily spoofed with objects as common as gummy candy.
Voice recognition systems tend to be the least accurate of the other biometric identification and authentication technologies. Voices can be readily recorded or mimicked, and allergies, colds and other respiratory issues can readily produce false negatives. Hand geometry and face recognition systems suffer from similar issues. They also tend to require a large amount of space and face recognition systems can be expensive. As with fingerprints, changes in a subject's skin, such as a suntan, a burn or a skin condition, or other changes to a subject's physical appearance can present problems for the system.